{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/davidbarker/Google Drive/Research/Facts and Premises/Premises BJPOLS Study 3 2018 Experiment 7 2020.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 8 Jul 2020, 07:10:28

{com}. do "/Users/davidbarker/Google Drive/Research/Facts and Premises/Premises BJPOLS Dataverse Study 3 2018 Experiment 7 2020.do"
{txt}
{com}. * stata version 15
. 
. * NOW FOR STUDY 3: 2018 DATA
. 
. use "/Users/davidbarker/Google Drive/Research/Facts and Premises/premis mturk BJPOLS dataverse mturk 2018 7 2020.dta"
{txt}(Written by R.              )

{com}. 
. *MAKING THE OUTCOME VARIABLE
. 
. alpha humansselfish humansdevious humansundiscip humansfail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0858182
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.6904
{txt}
{com}. factor humansselfish humansdevious humansundiscip humansfail, ml
{txt}(obs=2,939)
number of factors adjusted to {res}1
{txt}Iteration 0:   log likelihood = {res}-92.935113
{txt}Iteration 1:   log likelihood = {res}-1.2908233
{txt}Iteration 2:   log likelihood = {res} -1.283764

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}     2,939
{col 5}{txt}Method: maximum likelihood{col 50}Retained factors =   {res}       1
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       4
{col 50}{txt}Schwarz's BIC    =   {res} 34.5108
{col 5}{txt}Log likelihood = {res}-1.283764{col 50}{txt}(Akaike's) AIC   =   {res} 10.5675

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.49310            .            1.0000       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1932.73{txt} Prob>chi2 ={res} 0.0000
{txt}{col 5}LR test: {res}   1{txt} factor vs. saturated:  chi2({res}2{txt})  ={res}    2.57{txt} Prob>chi2 ={res} 0.2773

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:humansself~h}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6881}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5266}}}{space 1}
{space 4}{space 0}{ralign 12:humansdevi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7231}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4770}}}{space 1}
{space 4}{space 0}{ralign 12:humansundi~p}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5448}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7032}}}{space 1}
{space 4}{space 0}{ralign 12:humansfail}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4472}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8000}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. predict humansbadfactor01
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:humansself~h}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.35635}}}{space 1}
{space 4}{space 0}{ralign 12:humansdevi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41328}}}{space 1}
{space 4}{space 0}{ralign 12:humansundi~p}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.21126}}}{space 1}
{space 4}{space 0}{ralign 12:humansfail}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.15244}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}. sum humansbadfactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
humansbad~01 {c |}{res}      2,939   -5.72e-09    .8528181  -1.090166    1.21748
{txt}
{com}. replace humansbadfactor01 = humansbadfactor01 + 1.090166
{txt}(2,939 real changes made)

{com}. sum humansbadfactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
humansbad~01 {c |}{res}      2,939    1.090166    .8528181   2.66e-07   2.307646
{txt}
{com}. replace humansbadfactor01 = humansbadfactor01 / 2.307646
{txt}(2,939 real changes made)

{com}. sum humansbadfactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
humansbad~01 {c |}{res}      2,939    .4724148    .3695619   1.15e-07          1
{txt}
{com}. 
. *generating the interaction terms
. gen humansgoodpromptXlibcon301 = libcon301 * humansgoodprompt
{txt}(58 missing values generated)

{com}. gen humansbadpromptXlibcon301 = libcon301 * humansbadprompt
{txt}(58 missing values generated)

{com}. 
. * running the main regression model and generating the predicted values for the figure
. 
. reg humansbadfactor01 libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,558
{txt}{hline 13}{c +}{hline 34}   F(10, 1547)     = {res}    14.52
{txt}       Model {c |} {res} 18.7681652        10  1.87681652   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 199.978562     1,547  .129268624   {txt}R-squared       ={res}    0.0858
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0799
{txt}       Total {c |} {res} 218.746727     1,557  .140492439   {txt}Root MSE        =   {res} .35954

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         humansbadfactor01{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}libcon301 {c |}{col 28}{res}{space 2} .0433162{col 40}{space 2} .0361584{col 51}{space 1}    1.20{col 60}{space 3}0.231{col 68}{space 4}-.0276084{col 81}{space 3} .1142407
{txt}{space 10}humansgoodprompt {c |}{col 28}{res}{space 2}-.1514631{col 40}{space 2} .0293665{col 51}{space 1}   -5.16{col 60}{space 3}0.000{col 68}{space 4}-.2090655{col 81}{space 3}-.0938607
{txt}humansgoodpromptXlibcon301 {c |}{col 28}{res}{space 2} .1023131{col 40}{space 2} .0509427{col 51}{space 1}    2.01{col 60}{space 3}0.045{col 68}{space 4}  .002389{col 81}{space 3} .2022371
{txt}{space 11}humansbadprompt {c |}{col 28}{res}{space 2} .0340403{col 40}{space 2} .0292387{col 51}{space 1}    1.16{col 60}{space 3}0.245{col 68}{space 4}-.0233113{col 81}{space 3} .0913919
{txt}{space 1}humansbadpromptXlibcon301 {c |}{col 28}{res}{space 2} .1443406{col 40}{space 2} .0509148{col 51}{space 1}    2.83{col 60}{space 3}0.005{col 68}{space 4} .0444712{col 81}{space 3} .2442099
{txt}{space 20}female {c |}{col 28}{res}{space 2}-.0293307{col 40}{space 2} .0185895{col 51}{space 1}   -1.58{col 60}{space 3}0.115{col 68}{space 4} -.065794{col 81}{space 3} .0071326
{txt}{space 21}white {c |}{col 28}{res}{space 2} .0191635{col 40}{space 2} .0227058{col 51}{space 1}    0.84{col 60}{space 3}0.399{col 68}{space 4}-.0253738{col 81}{space 3} .0637009
{txt}{space 21}age01 {c |}{col 28}{res}{space 2}-.2112283{col 40}{space 2} .0481132{col 51}{space 1}   -4.39{col 60}{space 3}0.000{col 68}{space 4}-.3056023{col 81}{space 3}-.1168543
{txt}{space 11}incomecorrect01 {c |}{col 28}{res}{space 2} -.020665{col 40}{space 2} .0329445{col 51}{space 1}   -0.63{col 60}{space 3}0.531{col 68}{space 4}-.0852855{col 81}{space 3} .0439556
{txt}{space 13}educcorrect01 {c |}{col 28}{res}{space 2}-.0796106{col 40}{space 2} .0379152{col 51}{space 1}   -2.10{col 60}{space 3}0.036{col 68}{space 4}-.1539812{col 81}{space 3}-.0052399
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}   .59156{col 40}{space 2} .0427076{col 51}{space 1}   13.85{col 60}{space 3}0.000{col 68}{space 4} .5077891{col 81}{space 3} .6753309
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. sum humansbadfactor01

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
humansbadf~1 {c |}{res}      2,939    .4724148    .3695619   1.15e-07          1
{txt}
{com}. prvalue, x (libcon301=0 humansgoodprompt=0 humansbadprompt=0 humansgoodpromptXlibcon301=0 humansbadpromptXlibcon301=0)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22} .45556{col 32}{txt}[{res} .41467{txt},{res}{col 44} .49645{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           0             0             0             0             0     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. prvalue, x (libcon301=0 humansgoodprompt=1 humansbadprompt=0 humansgoodpromptXlibcon301=0 humansbadpromptXlibcon301=0)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22}  .3041{col 32}{txt}[{res} .26344{txt},{res}{col 44} .34475{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           0             1             0             0             0     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. prvalue, x (libcon301=1 humansgoodprompt=0 humansbadprompt=1 humansgoodpromptXlibcon301=0 humansbadpromptXlibcon301=0)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22} .53292{col 32}{txt}[{res} .45161{txt},{res}{col 44} .61423{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           1             0             0             1             0     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. prvalue, x (libcon301=1 humansgoodprompt=0 humansbadprompt=0 humansgoodpromptXlibcon301=0 humansbadpromptXlibcon301=0)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22} .49888{col 32}{txt}[{res} .44471{txt},{res}{col 44} .55305{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           1             0             0             0             0     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. prvalue, x (libcon301=1 humansgoodprompt=1 humansbadprompt=0 humansgoodpromptXlibcon301=1 humansbadpromptXlibcon301=0)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22} .44973{col 32}{txt}[{res}  .3959{txt},{res}{col 44} .50356{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           1             1             1             0             0     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. prvalue, x (libcon301=1 humansgoodprompt=0 humansbadprompt=1 humansgoodpromptXlibcon301=0 humansbadpromptXlibcon301=1)

{res}regress{txt}: Predictions for {res}humansbadfactor01

{col 32}{txt} 95% Conf. Interval
  Predicted y:{res}{col 22} .67726{col 32}{txt}[{res} .62221{txt},{res}{col 44} .73231{txt}]

       libcon301  humansgood~t  humansgo~301  humansbadp~t  humansba~301        female         white
x=  {res}           1             0             0             1             1     .45924262     .78818999

    {txt}       age01  incomecor~01  educcorre~01
x=  {res}   .33767938     .54946423     .69024391
{txt}
{com}. 
. * now estimating without demographic covariates:
. reg humansbadfactor01 libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 if interest3pt==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,560
{txt}{hline 13}{c +}{hline 34}   F(5, 1554)      = {res}    22.90
{txt}       Model {c |} {res} 15.0276008         5  3.00552017   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 203.955632     1,554   .13124558   {txt}R-squared       ={res}    0.0686
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0656
{txt}       Total {c |} {res} 218.983232     1,559  .140463908   {txt}Root MSE        =   {res} .36228

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         humansbadfactor01{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      t{col 60}   P>|t|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}libcon301 {c |}{col 28}{res}{space 2} .0383505{col 40}{space 2} .0361939{col 51}{space 1}    1.06{col 60}{space 3}0.289{col 68}{space 4}-.0326436{col 81}{space 3} .1093446
{txt}{space 10}humansgoodprompt {c |}{col 28}{res}{space 2}-.1474452{col 40}{space 2} .0295191{col 51}{space 1}   -4.99{col 60}{space 3}0.000{col 68}{space 4}-.2053465{col 81}{space 3}-.0895438
{txt}humansgoodpromptXlibcon301 {c |}{col 28}{res}{space 2} .0937734{col 40}{space 2}  .051236{col 51}{space 1}    1.83{col 60}{space 3}0.067{col 68}{space 4}-.0067257{col 81}{space 3} .1942724
{txt}{space 11}humansbadprompt {c |}{col 28}{res}{space 2} .0333998{col 40}{space 2} .0294231{col 51}{space 1}    1.14{col 60}{space 3}0.256{col 68}{space 4}-.0243134{col 81}{space 3} .0911131
{txt}{space 1}humansbadpromptXlibcon301 {c |}{col 28}{res}{space 2} .1381606{col 40}{space 2} .0511877{col 51}{space 1}    2.70{col 60}{space 3}0.007{col 68}{space 4} .0377563{col 81}{space 3} .2385649
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .4581304{col 40}{space 2} .0209421{col 51}{space 1}   21.88{col 60}{space 3}0.000{col 68}{space 4} .4170527{col 81}{space 3} .4992082
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * now just regressing the outcome variable on ideology, without the treatments
. reg humansbadfactor01 libcon301  female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,558
{txt}{hline 13}{c +}{hline 34}   F(6, 1551)      = {res}     8.71
{txt}       Model {c |} {res} 7.13379879         6  1.18896646   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 211.612928     1,551  .136436446   {txt}R-squared       ={res}    0.0326
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0289
{txt}       Total {c |} {res} 218.746727     1,557  .140492439   {txt}Root MSE        =   {res} .36937

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}humansbadfac~01{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      t{col 49}   P>|t|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}libcon301 {c |}{col 17}{res}{space 2} .1223143{col 29}{space 2} .0217313{col 40}{space 1}    5.63{col 49}{space 3}0.000{col 57}{space 4} .0796884{col 70}{space 3} .1649401
{txt}{space 9}female {c |}{col 17}{res}{space 2}-.0296813{col 29}{space 2}  .019095{col 40}{space 1}   -1.55{col 49}{space 3}0.120{col 57}{space 4}-.0671359{col 70}{space 3} .0077734
{txt}{space 10}white {c |}{col 17}{res}{space 2} .0151904{col 29}{space 2} .0233053{col 40}{space 1}    0.65{col 49}{space 3}0.515{col 57}{space 4}-.0305229{col 70}{space 3} .0609036
{txt}{space 10}age01 {c |}{col 17}{res}{space 2}-.1948978{col 29}{space 2} .0493871{col 40}{space 1}   -3.95{col 49}{space 3}0.000{col 57}{space 4}-.2917702{col 70}{space 3}-.0980253
{txt}incomecorrect01 {c |}{col 17}{res}{space 2}-.0227336{col 29}{space 2} .0337771{col 40}{space 1}   -0.67{col 49}{space 3}0.501{col 57}{space 4}-.0889872{col 70}{space 3}   .04352
{txt}{space 2}educcorrect01 {c |}{col 17}{res}{space 2}-.0764805{col 29}{space 2} .0389397{col 40}{space 1}   -1.96{col 49}{space 3}0.050{col 57}{space 4}-.1528605{col 70}{space 3}-.0001006
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .5489054{col 29}{space 2} .0403983{col 40}{space 1}   13.59{col 49}{space 3}0.000{col 57}{space 4} .4696643{col 70}{space 3} .6281466
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * now the probit models (coefficients displayed as differences in predicted probabilities) for each outcome variable that is included in the factor score index:
. 
. tab1 humansselfish humansdevious humansundiscip humansfail

{res}-> tabulation of humansselfish  

{txt}humansselfi {c |}
         sh {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,249       42.38       42.38
{txt}          1 {c |}{res}      1,698       57.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,947      100.00

-> tabulation of humansdevious  

{txt}humansdevio {c |}
         us {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,748       59.38       59.38
{txt}          1 {c |}{res}      1,196       40.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,944      100.00

-> tabulation of humansundiscip  

{txt}humansundis {c |}
        cip {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,387       47.16       47.16
{txt}          1 {c |}{res}      1,554       52.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,941      100.00

-> tabulation of humansfail  

 {txt}humansfail {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,944       66.14       66.14
{txt}          1 {c |}{res}        995       33.86      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,939      100.00
{txt}
{com}. 
. dprobit humansselfish libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}Iteration 0:   log likelihood = {res} -1067.562
{txt}Iteration 1:   log likelihood = {res}-1018.0853
{txt}Iteration 2:   log likelihood = {res}-1017.9682
{txt}Iteration 3:   log likelihood = {res}-1017.9682

{txt}Probit regression, reporting marginal effects           Number of obs ={res}   1558
                                                        {txt}LR chi2({res}10{txt})   ={res}  99.19
                                                        {txt}Prob > chi2   ={res} 0.0000
{txt}Log likelihood = {res}-1017.9682                             {txt}Pseudo R2     ={res} 0.0465

{txt}{hline 9}{c TT}{hline 68}
humans~h {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
libc~301 {c |}  {res}-.0070797   .0501614    -0.14   0.888   .370668  -.105394  .091235
{txt}humans..*{c |}  {res}-.2268388   .0400151    -5.55   0.000   .340822  -.305267 -.148411
{txt}humans.. {c |}  {res} .1460145   .0705946     2.07   0.039   .128049   .007652  .284377
{txt}humans..*{c |}  {res} .0147051   .0406996     0.36   0.718   .329268  -.065065  .094475
{txt}humans.. {c |}  {res} .2062901   .0729325     2.83   0.005   .119063   .063345  .349235
  {txt}female {c |}  {res}-.0066281   .0261087    -0.25   0.800   .459243    -.0578  .044544
   {txt}white*{c |}  {res}-.0019898   .0318688    -0.06   0.950    .78819  -.064451  .060472
   {txt}age01 {c |}  {res}-.1807321   .0677815    -2.67   0.008   .337679  -.313581 -.047883
{txt}incom~01 {c |}  {res}-.0288278    .046228    -0.62   0.533   .549464  -.119433  .061777
{txt}educc~01 {c |}  {res} -.064307   .0533535    -1.21   0.228   .690244  -.168878  .040264
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .5629012
{txt} pred. P {c |}  {res} .5660532{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
(*) dF/dx is for discrete change of dummy variable from 0 to 1
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0

{com}. dprobit humansdevious libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}Iteration 0:   log likelihood = {res}-1045.5634
{txt}Iteration 1:   log likelihood = {res}-991.73251
{txt}Iteration 2:   log likelihood = {res}-991.58688
{txt}Iteration 3:   log likelihood = {res}-991.58687

{txt}Probit regression, reporting marginal effects           Number of obs ={res}   1558
                                                        {txt}LR chi2({res}10{txt})   ={res} 107.95
                                                        {txt}Prob > chi2   ={res} 0.0000
{txt}Log likelihood = {res}-991.58687                             {txt}Pseudo R2     ={res} 0.0516

{txt}{hline 9}{c TT}{hline 68}
humans~s {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
libc~301 {c |}  {res} .0055896   .0496148     0.11   0.910   .370668  -.091654  .102833
{txt}humans..*{c |}  {res}-.1591765   .0389626    -3.93   0.000   .340822  -.235542 -.082811
{txt}humans.. {c |}  {res} .1633829    .070878     2.30   0.021   .128049   .024465  .302301
{txt}humans..*{c |}  {res}  .028301   .0401407     0.71   0.480   .329268  -.050373  .106975
{txt}humans.. {c |}  {res} .2127825   .0697058     3.05   0.002   .119063   .076162  .349403
  {txt}female {c |}  {res}-.0728399   .0257693    -2.83   0.005   .459243  -.123347 -.022333
   {txt}white*{c |}  {res} .0147678   .0311703     0.47   0.637    .78819  -.046325  .075861
   {txt}age01 {c |}  {res} -.321055   .0679093    -4.72   0.000   .337679  -.454155 -.187955
{txt}incom~01 {c |}  {res} .0139012   .0456964     0.30   0.761   .549464  -.075662  .103464
{txt}educc~01 {c |}  {res} -.123543   .0523824    -2.36   0.018   .690244  -.226211 -.020875
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3953787
{txt} pred. P {c |}  {res} .3897448{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
(*) dF/dx is for discrete change of dummy variable from 0 to 1
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0

{com}. dprobit humansundiscip libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}Iteration 0:   log likelihood = {res}-1077.5485
{txt}Iteration 1:   log likelihood = {res} -1041.329
{txt}Iteration 2:   log likelihood = {res} -1041.293
{txt}Iteration 3:   log likelihood = {res} -1041.293

{txt}Probit regression, reporting marginal effects           Number of obs ={res}   1558
                                                        {txt}LR chi2({res}10{txt})   ={res}  72.51
                                                        {txt}Prob > chi2   ={res} 0.0000
{txt}Log likelihood = {res} -1041.293                             {txt}Pseudo R2     ={res} 0.0336

{txt}{hline 9}{c TT}{hline 68}
humans~p {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
libc~301 {c |}  {res} .0749999   .0504905     1.49   0.137   .370668   -.02396   .17396
{txt}humans..*{c |}  {res}-.1364336   .0407871    -3.32   0.001   .340822  -.216375 -.056492
{txt}humans.. {c |}  {res} .1000821   .0713618     1.40   0.161   .128049  -.039784  .239949
{txt}humans..*{c |}  {res} .0961847   .0404243     2.36   0.018   .329268   .016954  .175415
{txt}humans.. {c |}  {res} .0081797    .071898     0.11   0.909   .119063  -.132738  .149097
  {txt}female {c |}  {res} .0070507   .0261828     0.27   0.788   .459243  -.044267  .058368
   {txt}white*{c |}  {res} .0758402   .0318553     2.38   0.018    .78819   .013405  .138275
   {txt}age01 {c |}  {res}-.2783822   .0682716    -4.08   0.000   .337679  -.412192 -.144572
{txt}incom~01 {c |}  {res}-.0880283   .0463806    -1.90   0.058   .549464  -.178933  .002876
{txt}educc~01 {c |}  {res} .0190957   .0532787     0.36   0.720   .690244  -.085329   .12352
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .5275995
{txt} pred. P {c |}  {res} .5282797{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
(*) dF/dx is for discrete change of dummy variable from 0 to 1
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0

{com}. dprobit humansfail libcon301 humansgoodprompt humansgoodpromptXlibcon301 humansbadprompt humansbadpromptXlibcon301 female white age01 incomecorrect01 educcorrect01 if interest3pt==2

{txt}Iteration 0:   log likelihood = {res}-976.99399
{txt}Iteration 1:   log likelihood = {res}-936.45328
{txt}Iteration 2:   log likelihood = {res}-936.42104
{txt}Iteration 3:   log likelihood = {res}-936.42104

{txt}Probit regression, reporting marginal effects           Number of obs ={res}   1558
                                                        {txt}LR chi2({res}10{txt})   ={res}  81.15
                                                        {txt}Prob > chi2   ={res} 0.0000
{txt}Log likelihood = {res}-936.42104                             {txt}Pseudo R2     ={res} 0.0415

{txt}{hline 9}{c TT}{hline 68}
humans~l {c |}{col 17}dF/dx{col 25}Std. Err.{col 40}z{col 45}P>|z|{col 55}x-bar{col 62}[    95% C.I.   ]
{hline 9}{c +}{hline 68}
libc~301 {c |}  {res} .2132119   .0464668     4.59   0.000   .370668   .122139  .304285
{txt}humans..*{c |}  {res} -.014714   .0395302    -0.37   0.711   .340822  -.092192  .062764
{txt}humans.. {c |}  {res}-.0938182   .0661595    -1.42   0.156   .128049  -.223488  .035852
{txt}humans..*{c |}  {res} .0166673   .0395167     0.42   0.672   .329268  -.060784  .094119
{txt}humans.. {c |}  {res} .0452134   .0653653     0.69   0.489   .119063    -.0829  .173327
  {txt}female {c |}  {res}-.0230174   .0243532    -0.95   0.345   .459243  -.070749  .024714
   {txt}white*{c |}  {res} .0099729   .0296355     0.34   0.738    .78819  -.048112  .068057
   {txt}age01 {c |}  {res} .0144883   .0629543     0.23   0.818   .337679    -.1089  .137877
{txt}incom~01 {c |}  {res}-.0067556   .0433585    -0.16   0.876   .549464  -.091737  .078225
{txt}educc~01 {c |}  {res}-.1503307   .0492103    -3.05   0.002   .690244  -.246781  -.05388
{txt}{hline 9}{c +}{hline 68}
  obs. P {c |}  {res} .3202824
{txt} pred. P {c |}  {res} .3140622{txt}  (at x-bar)
{hline 9}{c BT}{hline 68}
(*) dF/dx is for discrete change of dummy variable from 0 to 1
{p 4 0 0}z and P>|z| correspond to the test of the underlying coefficient being 0

{com}. 
{txt}end of do-file

{com}. log off
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